Enhancing Image Watermarking: An Innovative Multi-Objective Genetic Algorithm-Based DWT-SVD Approach for Robustness and Imperceptibility
Bibliographic record
Abstract
This paper investigates the enhancement of the image watermarking algorithm through its robustness and imperceptibility.We propose a watermarking method for protecting image data that is established using an optimal Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD).A multi-objective genetic algorithm (MOGA) with two conflicting objectives (i.e., PSNR and Hamming Distance (HD)) is employed to minimize the embedding distortion and maximize the robustness.These goals are achieved by merging natural selection and GA, producing a powerful tool for coefficients embedding optimization.GA has been used to guide the selection process of DWT coefficients for watermark embedding.We use Arnold transform to scramble the watermark bits to increase the watermark security.Consequently, various evaluation functions such as Peak signal-to-noise and SSIM are calculated to examine the watermarked image quality.Eventually, the selected coefficients represent the optimal choices to minimize the embedding distortion and maximize the robustness against attack.The final results of experiments demonstrate that the presented method is robust to many types of attacks, namely, Salt & Pepper, Gaussian, Speckle, Poisson, Resizing, Rotation, Median filter, cropping, and compression.The findings demonstrate that the suggested method performed better by implying embedding distortion and attack resistance than the current techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".